We use a two-layer neural network (with 128 hidden nodes) and the subset sampling scheme proposed by [1]} when calculating Shapley values. We compare the estimated Shapley values and the computing times when we use the retraining method (as is used in [1]}) and the lazy training method we proposed.
We calculate the Shapley values on 20 simulated datasets, each with a sample size 750. We split each dataset into a training set and test set with sample sizes 500 and 250 respectively. We train all the models on the training set, then evaluate the predictiveness loss and calculate the Shapley values on the test set. We set the penalty parameter as 50 in accordance to the assumption \(\lambda = O(\sqrt{n})\) .
